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Record W4308342039 · doi:10.1093/jcmc/zmac023

Darknet imaginaries in Internet memes: the discursive malleability of the cultural status of digital technologies

2022· article· en· W4308342039 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Computer-Mediated Communication · 2022
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsYork University
FundersNarodowe Centrum Nauki
KeywordsFlourishingMainstreamSociologyMalleabilitySocial mediaArticulation (sociology)Digital mediaPoliticsSign (mathematics)Media studiesThe InternetComputer scienceWorld Wide WebPolitical sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

Abstract Dominant discourses on the darknet present it either as a dangerous space with flourishing crime or a place for civic action and political activism. However, these depictions have been challenged in online popular culture, particularly in memes. By utilizing the concepts of double articulation of media and cultural imaginaries, this article reveals how memes shape popular definitions of darknet. Our qualitative, social semiotic content analysis of 505 memes reveals an ambiguous and complex vision of the darknet that both supports and demystifies the mainstream imagery. We introduce the concept of discursive malleability of niche technologies to describe how cultural practices reshape technologies, especially those with small userbases. Additionally, we present a “representational map of the darknet” and indicate how this contributes to social understanding of digital technologies more generally, and, not least why the analyzed memes may be read as lens exposing contradictory notions and policies regarding digital technologies nowadays.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.305
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it